%matplotlib inline

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import cm

import ipyparallel as ipp

from time import time
from datetime import datetime

import motif as mf

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.decomposition import PCA
from sklearn.utils import shuffle
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.model_selection import train_test_split, cross_val_score, cross_validate

from scipy.stats import spearmanr
from scipy.stats import pearsonr

Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)

### set parameters for the motif analysis

PROTEIN_NAME = 'Mlx'
PROT_CONC = 0.1  # free protein concentration at binding reation; PBM typically 0.1 and RNACompete typically 0.002
BOTH_STRANDS = True  # wheter both strands are present for binding; True if double-stranded DNA or RNA is used as probes
#TIME_DISS = 1800  # experimental time span after binding reaction during which dissociation of the protein from the probe was possible

STAGES=mf.stage(protein=PROTEIN_NAME)

### read data

## RNAcompete sample data
#dfprobes_raw=pd.read_excel('./data/RNAcompete/A2BP1.xlsx')
#dfprobes_raw=pd.read_excel('./data/RNAcompete/HNRNPA1.xlsx')
#dfprobes_raw=pd.read_excel('./data/RNAcompete/PTBP1.xlsx')
#dfprobes_raw=pd.read_excel('./data/RNAcompete/RBM24.xlsx')


dfprobes_raw=pd.read_csv('./data/samplePBMs/Mlx__pTH2882_HK.raw', sep='\t')
#dfprobes_raw=pd.read_csv('./data/samplePBMs/Klf9__pTH2353_HK.raw', sep='\t')
#dfprobes_raw=pd.read_csv('./data/samplePBMs/Prdm11__pTH3455_HK.raw', sep='\t')
#dfprobes_raw=pd.read_csv('./data/samplePBMs/Sox10__pTH1729_HK.raw', sep='\t')


print('Columns of imported Data File: %s' % dfprobes_raw.columns)
#dfprobes_raw.describe()
#dfprobes_raw.info()

Columns of imported Data File: Index(['#id_spot', 'row', 'col', 'control', 'id_probe', 'pbm_sequence',
       'linker_sequence', 'mean_signal_intensity', 'mean_background_intensity',
       'flag'],
      dtype='object')

### select columns for probe sequence and signal

column_sequence = 'pbm_sequence'
column_signal = 'mean_signal_intensity'
background_signal = 'mean_background_intensity'  #set to None if not needed
#background_signal=None

#basic preprocessing
dfprobes_raw[column_signal] = dfprobes_raw[column_signal].apply(
    lambda a: np.NaN if a == ' ' else a)
dfprobes_raw[column_signal] = dfprobes_raw[column_signal].apply(
    lambda a: np.NaN if a == '' else a)
dfprobes_raw[column_sequence] = dfprobes_raw[column_sequence].apply(
    lambda a: np.NaN if str(a).lower() == 'nan' else a)
dfprobes_raw[column_sequence] = dfprobes_raw[column_sequence].apply(
    lambda a: np.NaN if a == '' else a)
dfprobes_raw = dfprobes_raw.dropna()

#construct new dataframe with only necessary data
if type(background_signal) == type(None):
    dfprobes = pd.DataFrame({
        'seq':
        dfprobes_raw[column_sequence].astype(str),
        'signal binding':
        dfprobes_raw[column_signal].astype(np.float32)
    })  #rebuild dataframe
else:
    dfprobes = pd.DataFrame({
        'seq':
        dfprobes_raw[column_sequence].astype(str),
        'signal':
        dfprobes_raw[column_signal].astype(np.float32),
        'background':
        dfprobes_raw[background_signal].astype(np.float32)
    })  #rebuild dataframe
    dfprobes['signal binding'] = dfprobes['signal'] - dfprobes['background']

dfprobes = dfprobes.dropna()    

    
# display main properties of data set
dfprobes['signal binding'].plot(figsize=(15, 5))
dfprobes.describe()

### check type of nucleic acid

dfprobes['seq'] = dfprobes['seq'].apply(
    lambda seq: seq.upper().replace(" ", ""))  #upper and remove blanks
dfprobes['RNA'] = dfprobes['seq'].apply(
    lambda seq: all(char in 'ACGU' for char in seq))
dfprobes['DNA'] = dfprobes['seq'].apply(
    lambda seq: all(char in 'ACGT' for char in seq))
non_RNA_counts = len(dfprobes[dfprobes['RNA'] == False])
non_DNA_counts = len(dfprobes[dfprobes['DNA'] == False])

if non_RNA_counts < non_DNA_counts:
    NUC_TYPE = 'RNA'
    print('I: RNA probes detected!')
else:
    NUC_TYPE = 'DNA'
    print('I: DNA probes detected!')

if NUC_TYPE == 'RNA' and non_RNA_counts != 0:
    print(
        'E: The probe sequences appear to be RNA, however there are some non-RNA nucleotides in the sequences.'
    )
    print('E: Please check the following sequnces %s' %
          dfprobes[dfprobes['RNA'] == False])

if NUC_TYPE == 'DNA' and non_DNA_counts != 0:
    print(
        'E: The probe sequences appear to be RNA, however there are some non-RNA nucleotides in the sequences.'
    )
    print('E: Please check the following sequnces %s' %
          dfprobes[dfprobes['DNA'] == False])

I: DNA probes detected!

### option to add a constant sequence at the 3' end and 5' end
sequence_to_be_added_5 = ''
sequence_to_be_added_3 = 'CCTGT'  # standard PBM arrays: CCTGTGTGAAATTGTTATCCGCTCT T7 array: GTCTTGA..
dfprobes['seq'] = sequence_to_be_added_5.upper(
) + dfprobes['seq'] + sequence_to_be_added_3.upper()
print(
    f"I: The nucleotide sequence {sequence_to_be_added_5.upper()} has been added to the 5' end all probe sequences."
)
print(
    f"I: The nucleotide sequence {sequence_to_be_added_3.upper()} has been added to the 3' end all probe sequences."
)

I: The nucleotide sequence  has been added to the 5' end all probe sequences.
I: The nucleotide sequence CCTGT has been added to the 3' end all probe sequences.

### egalize length
dfprobes['seq_length'] = dfprobes['seq'].apply(len)

if max(dfprobes['seq_length']) != min(dfprobes['seq_length']):
    print('I: Probes length is not uniform, detected range: %i ..%i' %
          (min(dfprobes['seq_length']), max(dfprobes['seq_length'])))
    max_length = max(dfprobes['seq_length'])
    dfprobes['padded_sequence'] = dfprobes['seq'].apply(
        lambda seq: seq + ((max_length - len(seq)) * '-'))
    print(
        "I: Probe sequences have been padded at the 5' to the uniform length of %i nucleotides"
        % max_length)
else:
    print('I: Probe sequences have the uniform length of %i nucleotides' %
          (dfprobes['seq_length'].median()))
    dfprobes['padded_sequence'] = dfprobes['seq']

print('I: Total datasets contains %i sequences.' % len(dfprobes))

# visualize composition of each position
df_nucleotides = mf.split_sequence_in_nucleotides(dfprobes['seq'])
dfcount = pd.DataFrame(index=['A', 'C', 'G', 'T', 'U'])
for column in df_nucleotides:
    dfcount[column] = df_nucleotides[column].value_counts()
dfcount = dfcount.fillna(0)  #zeros for NaN
dfcount.transpose().plot(figsize=(15, 5), kind='bar')
print('I: Visualisation of the base composition per position')
print(
    'I: If positions are invariant they can be removed before sequence analysis.'
)

I: Probes length is not uniform, detected range: 36 ..40
I: Probe sequences have been padded at the 5' to the uniform length of 40 nucleotides
I: Total datasets contains 40330 sequences.
I: Visualisation of the base composition per position
I: If positions are invariant they can be removed before sequence analysis.

# You may remove invariant continuos positions by adjusting the slicing.
# It is recommended to leave a few invariant positions to allow for binding events
# between the variable and constant part of the probes.

dfprobes['seq'] = dfprobes['seq'].apply(lambda s: s[:40])  ### <==== do the slicing here

# visualize composition of each position
print('I: Visualisation of the base composition per position after slicing.')
df_nucleotides = mf.split_sequence_in_nucleotides(dfprobes['seq'])
dfcount = pd.DataFrame(index=['A', 'C', 'G', 'T', 'U'])
for column in df_nucleotides:
    dfcount[column] = df_nucleotides[column].value_counts()
dfcount = dfcount.fillna(0)  #zeros for NaN
dfcount.transpose().plot(figsize=(15, 5), kind='bar')
plt.show()

# preparation for later classification
mean = dfprobes['signal binding'].mean()
std = dfprobes['signal binding'].std()
THRESHOLD = mean + 4 * std  #4*std used according to Weirauch et al., 2013
dfprobes['positive probe'] = dfprobes['signal binding'].apply(
    lambda s: True if s > THRESHOLD else False)

print(
    'I: The whole dataset has been used to set the threshold for a positive probe.'
)
print('I: The threshold is %f' % THRESHOLD)
print(
    f"I: {len(dfprobes[dfprobes['positive probe']])} probes of {len(dfprobes)} are above threshold."
)

if len(dfprobes[dfprobes['positive probe']]) == 0:
    print(
        'E: No probe above THRESHOLD. Classification is not possible. Please adjust the THRESHOLD.'
    )

I: Visualisation of the base composition per position after slicing.

I: The whole dataset has been used to set the threshold for a positive probe.
I: The threshold is 23991.450195
I: 524 probes of 40330 are above threshold.

### Shuffle and prepare dataset for training and testing

# shuffle and split
dfprobes = shuffle(dfprobes)
dftrain, dftest = train_test_split(dfprobes, test_size=0.2)

print(
    'I: The whole dataset has been split in training (80%) and test (20%) datasets.'
)

# display histogramms of test and training set
dftrain['signal binding'].plot(kind='hist', bins=25).axvline(x=THRESHOLD, color='r', linestyle='-.', lw=0.5, label='threshold classification')
dftest['signal binding'].plot(kind='hist', bins=25)
plt.show()

# generate a subset with maximal 1000 probes

downsampled_size = 1000  # You may change downsampled size here.

percentile = 0.5 * downsampled_size / len(
    dftrain
) * 100  #percentile required for lowest and highest to achieve down-sampled size
if percentile < 4:
    percentile = 4  #do not use only the extreme values
elif percentile > 10:
    percentile = 10  #avoid taking value from the mid-range

if len(dftrain) * percentile * 2 / 100 < downsampled_size / 4:
    print('W: The subset only contains %i probes - a rather low number.' %
          dftrain * percentile * 2 / 100)

print(
    'I: A downsampled dataset containing the lowest and highest %.1f %% of the dataset is generated.'
    % percentile)
dfsubset_high = dftrain[dftrain['signal binding'] >= dftrain['signal binding'].quantile(1 - percentile / 100)]  # highest part
dfsubset_low = dftrain[dftrain['signal binding'] <= dftrain['signal binding'].quantile(percentile / 100)]  # lowest part
print('I: Median values of lowest and highest %.1f %%:  %r  %r' %
      (percentile, dfsubset_low['signal binding'].quantile(0.5),
       dfsubset_high['signal binding'].quantile(0.5)))

if len(dfsubset_high) + len(dfsubset_low) > downsampled_size:
    print('I: The dataset is further downsampled to %i sequences.' %
          downsampled_size)
    dfsubset_high = dfsubset_high.sample(downsampled_size - int(downsampled_size / 2))
    dfsubset_low = dfsubset_low.sample(int(downsampled_size / 2))
    dfsubset = pd.concat([dfsubset_high, dfsubset_low])
else:
    dfsubset = pd.concat([dfsubset_high, dfsubset_low])

dfsubset = shuffle(dfsubset)   
    
# display main properties of downsampled data set
print('I: Histogramm of the downsampled dataset along the with classification threshold.')
dfsubset['signal binding'].plot(kind='hist', bins=25).axvline(x=THRESHOLD, color='r', linestyle='-.', lw=0.5, label='threshold classification')
plt.show()

# establish numpy arrays of the sequenc and binding data in the dataframes

# complete data
X=mf.hotencode_sequence(dfprobes['padded_sequence'], nuc_type=NUC_TYPE)
y=np.array(dfprobes['signal binding'])

# training set
X_train=mf.hotencode_sequence(dftrain['padded_sequence'], nuc_type=NUC_TYPE)
y_train=np.array(dftrain['signal binding'])

# subset of training set
X_subset=mf.hotencode_sequence(dfsubset['padded_sequence'], nuc_type=NUC_TYPE)
y_subset=np.array(dfsubset['signal binding'])

# test set
X_test=mf.hotencode_sequence(dftest['padded_sequence'], nuc_type=NUC_TYPE)
y_test=np.array(dftest['signal binding'])

I: The whole dataset has been split in training (80%) and test (20%) datasets.

I: A downsampled dataset containing the lowest and highest 4.0 % of the dataset is generated.
I: Median values of lowest and highest 4.0 %:  1064.994384765625  16825.373046875
I: The dataset is further downsampled to 1000 sequences.
I: Histogramm of the downsampled dataset along the with classification threshold.

### perform a quick & dirty round for a short motif by fitting on subset to check data integrity

#fit regression quick_model
quick_model=mf.findmotif(motif_length=3, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS, ftol=0.01)

start = time()
quick_model.fit(X_subset,y_subset)
print("I: Optimization took %.2f hours." % ((time() - start)/3600))

# print & display main results
quick_model.analyse_motif(X_subset,y_subset, THRESHOLD, nuc_type=NUC_TYPE)

# store results and display stages
STAGES.append('quick', quick_model)
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

WARNING:matplotlib.font_manager:findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.

I: Optimization took 0.05 hours.
I: energy matrix and logos:

        A     C     G      T
0  -1722  5146 -5630   2206
1  16314  5041 -4952 -16403
2   7482  -739 -5187  -1555

I: summed absolute energies of each position:
0    14705
1    42712
2    14964
dtype: int64

I: averaged summed energy over all positions: 24127
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -6069 +/- 14080
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.08408 .. 2.54079 (ratio: 30.2)
I: number of probes: 1000
I: Pearson Correlation  r: 0.6651
I: mean absolute error: 7692.7441

I: Classification performance AUROC: 0.8283

	stage	protein	# probes	motif length	r	AUROC	G0	G0 fitted	ratio	max binding	min binding	energies	model	logo
0	quick	Mlx	1000	3	0.665118	0.828279	-11473.366709	False	30.220111	2.54079	0.084076	-1722,..	suppressed

quick_model.explore_positions(X_train, y_train)

	pos	energies	r	r under baseline	-2%
0	0	[-0.0, 0.0, -0.0, 0.0, 16314.725740130505, 504...	0.184025	-0.481093	True
1	1	[-1722.0232094965108, 5146.111079001937, -5630...	0.041719	-0.623399	True
2	2	[-1722.0232094965108, 5146.111079001937, -5630...	0.213306	-0.451812	True

#### Perfrom GridCV Search for exploration of the motif length goal: identify the minimum motif length which gives a good r-value

# optional: allow for global optimization to verify whether the local optimization is good enough
# not recommended include fitG0=True. This option should only be considered when the local optimization is started with an approximate motif and the start parameter is set
# not recommended set time_dissociation. The effect of dissociation should be only considered when the local optimization is started with an approximate motif.


# prepare grid search over motif_length
model_grid=mf.findmotif(protein_conc=PROT_CONC, both_strands=BOTH_STRANDS)
param_grid = {"motif_length": [3,4,5,6,7]}     # choose sensible range for length of motif

# define custom refit function
def custom_refit(cv_results):
    """returns index of max r2/sqrt(motif_length)"""
    df_grid=pd.DataFrame(cv_results)
    index=(df_grid['mean_test_score']/(df_grid['param_motif_length'].apply(float).apply(np.sqrt))).idxmax()
    return index

# run grid search and refit according to custom refit
grid_search = GridSearchCV(model_grid, param_grid=param_grid, verbose=2, cv=5, refit=custom_refit, n_jobs=-1)

start = time()
grid_search.fit(X_subset, y_subset)

print("I: GridSearchCV took %.2f hours for %d candidate parameter settings."
    % ((time() - start)/3600, len(grid_search.cv_results_["params"])))
print('I: number of samples: %i' %len(X_subset))

df_grid=pd.DataFrame(grid_search.cv_results_)
print('I: Plot of r2 vs motif length and vs root(motif length)')
df_grid.rename(columns={'mean_test_score':'r2'}, inplace=True)
df_grid.plot(kind='scatter', x='param_motif_length', y='r2', yerr='std_test_score', figsize=(5,3)).set_xticks(param_grid["motif_length"])
df_grid['r2/sqrt(length)']=df_grid['r2']/(df_grid['param_motif_length'].apply(float).apply(np.sqrt))
df_grid['std/sqrt(length)']=df_grid['std_test_score']/(df_grid['param_motif_length'].apply(float).apply(np.sqrt))
df_grid.plot(kind='scatter', x='param_motif_length', y='r2/sqrt(length)',yerr='std/sqrt(length)', figsize=(5,3)).set_xticks(param_grid["motif_length"])
plt.show()

best_index=df_grid['r2/sqrt(length)'].idxmax()
CORE_MOTIF_LENGTH=df_grid.loc[best_index, 'param_motif_length']
print(f'I: The maximum ({CORE_MOTIF_LENGTH}) is suggested as CORE_MOTIF_LENGTH')

print('I: motif obtained with the best estimator from gridCV search')
# print & display results from best estimator
model_grid=grid_search.best_estimator_
model_grid.analyse_motif(X_subset,y_subset, THRESHOLD, nuc_type=NUC_TYPE)

# store results and display stages
STAGES.append('best grid', model_grid)
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

Fitting 5 folds for each of 5 candidates, totalling 25 fits
I: GridSearchCV took 0.86 hours for 5 candidate parameter settings.
I: number of samples: 1000
I: Plot of r2 vs motif length and vs root(motif length)

I: The maximum (6) is suggested as CORE_MOTIF_LENGTH
I: motif obtained with the best estimator from gridCV search
I: energy matrix and logos:

        A     C     G      T
0   2367 -8160  2460   3332
1  -2164  3681 -8941   7423
2  11906  2282   548 -14737
3   1648  4195 -9490   3646
4    802  1154  1269  -3225
5   1001    39   -95   -946

I: summed absolute energies of each position:
0    16320
1    22211
2    29474
3    18980
4     6451
5     2083
dtype: int64

I: averaged summed energy over all positions: 15920
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -1372 +/- 13555
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.00141 .. 3.19934 (ratio: 2264.7)
I: number of probes: 1000
I: Pearson Correlation  r: 0.8042
I: mean absolute error: 5358.4744

I: Classification performance AUROC: 0.9031

	stage	protein	# probes	motif length	r	AUROC	G0	G0 fitted	ratio	max binding	min binding	energies	model	logo
0	quick	Mlx	1000	3	0.665118	0.828279	-11473.366709	False	30.220111	2.540790	0.084076	-1722,..	suppressed
1	best grid	Mlx	1000	6	0.804184	0.903057	-1946.454775	False	2264.716271	3.199344	0.001413	2367,..	suppressed

### run a number of identical optimizations with motif length found during grid search
### goal: find best motif through repetition, judge stabiltiy of optimization

#CORE_MOTIF_LENGTH=5  # adjust core motif length if needed, motif length can be changed later

# prepare for ipyparallel
number_of_optimizations = 20
model_list = [mf.findmotif(motif_length=CORE_MOTIF_LENGTH, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS)] * number_of_optimizations
X_list = [X_subset] * number_of_optimizations
y_list = [y_subset] * number_of_optimizations


def single_job(model, X, y):
    model.fit(X, y)
    return {'model':model}

# run the optimizations on ipp.cluster
start = time()
with ipp.Cluster(log_level=40) as rc:
    rc[:].use_pickle()
    view = rc.load_balanced_view()
    asyncresult = view.map_async(single_job, model_list, X_list, y_list)
    asyncresult.wait_interactive()
    result = asyncresult.get()
print("I: Optimization took %.2f hours." % ((time() - start) / 3600))


  
# assemble results and analyze
df_repetitions=pd.DataFrame(result)
df_repetitions['r (subset)']=df_repetitions['model'].apply(lambda e: e.rvalue)
df_repetitions['r (train)']=df_repetitions['model'].apply(lambda e: mf.linregress(e.predict(X_train),y_train).rvalue)
df_repetitions['r (test)']=df_repetitions['model'].apply(lambda e: mf.linregress(e.predict(X_test),y_test).rvalue)
df_repetitions['G0']=df_repetitions['model'].apply(lambda e: e.finalG0_)
df_repetitions['max binding']=df_repetitions['model'].apply(lambda e: e.max_binding_fit)
df_repetitions['min binding']=df_repetitions['model'].apply(lambda e: e.min_binding_fit)
df_repetitions['ratio'] = df_repetitions['model'].apply(lambda e: e.ratio)
df_repetitions['energies']=df_repetitions['model'].apply(lambda e: e.energies_)
#df_repetitions['information']=df_repetitions['model'].apply(lambda e: mf.energies2information(e.energies_))


# display results of the ensemble of optimizations
print('I: Results of the repeated motif finding, sorted according to the regression coefficient with the train dataset')
df_repetitions.sort_values('r (train)', ascending=False, inplace=True)
mf.display_df(df_repetitions, nuc_type=NUC_TYPE)

  0%|          | 0/16 [00:00<?, ?engine/s]

single_job:   0%|          | 0/20 [00:00<?, ?tasks/s]

I: Optimization took 1.03 hours.
I: Results of the repeated motif finding, sorted according to the regression coefficient with the train dataset

	model	r (subset)	r (train)	r (test)	G0	max binding	min binding	ratio	energies	logo
7	suppressed	0.890235	0.732795	0.732571	-1946.454775	0.172153	0.001413	121.794730	-3592,..
16	suppressed	0.889981	0.728138	0.726752	-1946.454775	0.022200	0.000176	125.990337	-841,..
17	suppressed	0.888720	0.727082	0.727752	-1946.454775	1.412396	0.002897	487.528460	267,..
4	suppressed	0.887252	0.723821	0.724293	-1946.454775	1.370802	0.005552	246.880343	-6852,..
12	suppressed	0.868733	0.611553	0.624722	-1946.454775	2.853511	0.019868	143.626054	-4857,..
3	suppressed	0.868782	0.611051	0.624704	-1946.454775	2.935046	0.018557	158.165610	-5107,..
10	suppressed	0.866805	0.609516	0.622133	-1946.454775	2.831423	0.014889	190.171324	-4950,..
2	suppressed	0.866651	0.606835	0.620001	-1946.454775	3.001508	0.017313	173.366242	-4981,..
18	suppressed	0.859843	0.603754	0.616578	-1946.454775	2.779731	0.003339	832.585500	443,..
8	suppressed	0.865081	0.602153	0.616200	-1946.454775	2.761261	0.005642	489.396493	179,..
19	suppressed	0.855076	0.600044	0.609233	-1946.454775	2.750740	0.050765	54.185733	-9490,..
14	suppressed	0.804147	0.492140	0.501041	-1946.454775	3.265180	0.001223	2669.669826	2621,..
1	suppressed	0.804646	0.491672	0.500635	-1946.454775	3.310548	0.001151	2877.189644	2596,..
9	suppressed	0.803433	0.491532	0.500840	-1946.454775	3.162870	0.001296	2441.333598	2188,..
5	suppressed	0.803785	0.491476	0.500087	-1946.454775	3.193325	0.001248	2558.910713	1710,..
15	suppressed	0.802353	0.490766	0.501396	-1946.454775	3.265955	0.001267	2577.979257	10698,..
0	suppressed	0.645968	0.320167	0.310050	-1946.454775	0.092373	0.002677	34.505616	-3483,..
6	suppressed	0.677981	0.313893	0.311648	-1946.454775	3.318715	0.004870	681.456664	443,..
13	suppressed	0.681568	0.311863	0.308358	-1946.454775	2.771487	0.005565	498.020474	-6381,..
11	suppressed	0.602044	0.276270	0.299125	-1946.454775	3.731195	0.167081	22.331606	-11311,..

### compare energy matrices of ensemble using PCA
print('I: Histogram of the regression coefficients r obtained by repeated optimizaion with the subset.')
df_repetitions['r (subset)'].plot(kind='hist')
plt.show()

pca = PCA(n_components=2)
pca_2c=pca.fit_transform(df_repetitions['energies'].tolist())    
df_repetitions[['PCA1', 'PCA2']]=pca_2c
print('I: 2-dimensional PCA explained %i %% of variance.' %(sum(pca.explained_variance_ratio_)*100))
if sum(pca.explained_variance_ratio_)<0.5:
      print('W: 2-dimensional PCA explained only %i %% of variance' %(sum(pca.explained_variance_ratio_)*100))

print('I: Visualization of the PCA with the regression quality vs. subset and training dataset by color.')        
df_repetitions.plot(x='PCA1', y='PCA2', kind='scatter', c='r (subset)',cmap=cm.coolwarm, edgecolors='black', linewidths=0.3)
df_repetitions.plot(x='PCA1', y='PCA2', kind='scatter', c='r (train)',cmap=cm.coolwarm, edgecolors='black', linewidths=0.3)

I: Histogram of the regression coefficients r obtained by repeated optimizaion with the subset.

I: 2-dimensional PCA explained 62 % of variance.
I: Visualization of the PCA with the regression quality vs. subset and training dataset by color.

/home/GLipps/.local/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute `n_features_` was deprecated in version 1.2 and will be removed in 1.4. Use `n_features_in_` instead.
  warnings.warn(msg, category=FutureWarning)

<matplotlib.axes._subplots.AxesSubplot at 0x7f698d609ee0>

# visualisation of the motif with the highest r with the train dataset
print('I: Best motif according to r (train) from the repeated optimizations.')
print('I: PCA components: %i, %i' %(df_repetitions.iloc[0]['PCA1'], df_repetitions.iloc[0]['PCA2']))
model_best_repetition=df_repetitions.iloc[0]['model']
model_best_repetition.analyse_motif(X_subset,y_subset, THRESHOLD, nuc_type=NUC_TYPE) 
# store results and display stages
STAGES.append('best repetition', model_best_repetition, new_entries={'r (test)': mf.linregress(model_best_repetition.predict(X_test),y_test).rvalue})
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

I: Best motif according to r (train) from the repeated optimizations.
I: PCA components: -19629, -10097
I: energy matrix and logos:

        A     C      G      T
0  -3592 -8307  16172  -4272
1 -11035 -3042  -3116  17194
2   4214 -4730   2665  -2149
3   -689  1418  -3579   2851
4    360   939    -57  -1242
5   2109   810  -2257   -662

I: summed absolute energies of each position:
0    32345
1    34389
2    13760
3     8538
4     2599
5     5839
dtype: int64

I: averaged summed energy over all positions: 16245
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -1251 +/- 14924
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.00141 .. 0.17215 (ratio: 121.8)
I: number of probes: 1000
I: Pearson Correlation  r: 0.8902
I: mean absolute error: 4098.7474

I: Classification performance AUROC: 0.9316

	stage	protein	# probes	motif length	r	AUROC	G0	G0 fitted	ratio	max binding	min binding	energies	model	logo	r (test)
0	quick	Mlx	1000	3	0.665118	0.828279	-11473.366709	False	30.220111	2.540790	0.084076	-1722,..	suppressed		NaN
1	best grid	Mlx	1000	6	0.804184	0.903057	-1946.454775	False	2264.716271	3.199344	0.001413	2367,..	suppressed		NaN
2	best repetition	Mlx	1000	6	0.890235	0.931599	-1946.454775	False	121.794730	0.172153	0.001413	-3592,..	suppressed		0.732571

### motif finding on complete training dataset starting with best motif from repetitions

#fit & predict optimization starting with previous energy matrix
model_train=mf.findmotif(motif_length=CORE_MOTIF_LENGTH, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS,
                   start=model_best_repetition.energies_)
start = time()
model_train.fit(X_train,y_train)
print("I: Optimization took %.2f hours." % ((time() - start)/3600))

# print & display main results
model_train.analyse_motif(X_train,y_train, THRESHOLD, nuc_type=NUC_TYPE)

# store results and display stages
STAGES.append('train dataset', model_train, new_entries={'r (test)': mf.linregress(model_train.predict(X_test),y_test).rvalue})
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

I: Optimization took 7.41 hours.
I: energy matrix and logos:

       A     C     G     T
0  -836 -8045  6487  2394
1 -8420  2590  2697  3132
2  3329 -5229  4091 -2190
3 -1975  4342 -6715  4347
4   833   511  1285 -2630
5  1395  2287 -3832   150

I: summed absolute energies of each position:
0    17765
1    16840
2    14841
3    17381
4     5260
5     7665
dtype: int64

I: averaged summed energy over all positions: 13292
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -872 +/- 9975
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.00024 .. 0.56984 (ratio: 2338.7)
I: number of probes: 32264
I: Pearson Correlation  r: 0.8077
I: mean absolute error: 1562.0631

I: Classification performance AUROC: 0.9813

	stage	protein	# probes	motif length	r	AUROC	G0	G0 fitted	ratio	max binding	min binding	energies	model	logo	r (test)
0	quick	Mlx	1000	3	0.665118	0.828279	-11473.366709	False	30.220111	2.540790	0.084076	-1722,..	suppressed		NaN
1	best grid	Mlx	1000	6	0.804184	0.903057	-1946.454775	False	2264.716271	3.199344	0.001413	2367,..	suppressed		NaN
2	best repetition	Mlx	1000	6	0.890235	0.931599	-1946.454775	False	121.794730	0.172153	0.001413	-3592,..	suppressed		0.732571
3	train dataset	Mlx	32264	6	0.807686	0.981296	-1946.454775	False	2338.717644	0.569843	0.000244	-836,..	suppressed		0.805514

### Based on the motif of CORE_MOTIF_LENGTH analyze the neigbouring positions 
### whether their inclusion can improve the quality of the motif
df_positions=model_train.investigate_extension_parallel(X_train,y_train, end5=3, end3=3, nuc_type=NUC_TYPE)

list_positions=df_positions.index[df_positions['+2%']].tolist()+[0] # list of positions with an increase of2% and default position 0
ext5=-min(list_positions)
ext3=max(list_positions)
print("I: It is suggested to extend the core motif at the 5' end by %i and at the 3' end by %i positions." %(ext5, ext3))

### Analyze model whether the estimated G0 is correct
#df_G0=model_train.investigate_G0(X_train,y_train)

  0%|          | 0/6 [00:00<?, ?engine/s]

job5:   0%|          | 0/3 [00:00<?, ?tasks/s]

job3:   0%|          | 0/3 [00:00<?, ?tasks/s]

I: Optimization took 0.27 hours.

I: It is suggested to extend the core motif at the 5' end by 0 and at the 3' end by 0 positions.
I: Current G0 = -1946 J/mol (see red broken line in figure below) with r = 0.808.
I: Maximal r is 0.808 at G0=-1946 J/mol (see green broken line below).
I: Maximal occupancy of 2 is reached at G0=-7946 J/mol (see blue broken line below).
I: Maximal occupancy of 0.2 is reached at G0=1054 J/mol (see blue broken line below).

I: G0 is in a range leading to maximal probe occupancy between 0.2 and 2. Good.
I: Maximal r is close to r achieved with current G0. Good.

### fit & predict optimization starting with extended energy matrix if extension appears to improve prediction

if ext5+ext3!=0: #extension suggestion from previous analysis of the bordering positions
    expanded_energies=model_train.energies_
    # append energies of single-optimized bordering positions to energies of central part
    if ext5!=0:
        energies_5=np.concatenate(df_positions['energies'][(df_positions.index<0) & (df_positions.index>=-ext5)].to_numpy())
        expanded_energies=np.concatenate((energies_5, expanded_energies))
    if ext3!=0:
        energies_3=np.concatenate(df_positions['energies'][(df_positions.index<=ext3) & (df_positions.index>0)].to_numpy().flatten())
        expanded_energies=np.concatenate((expanded_energies,  energies_3))

    mf.energies2logo(expanded_energies, nuc_type=NUC_TYPE)
    print('I: Optimization started with following extended motif.')
    expanded_motif_length=len(expanded_energies)//4
    
    
    model_extended=mf.findmotif(motif_length=expanded_motif_length, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS,
                   start=expanded_energies)

    start = time()
    model_extended.fit(X_train,y_train)
    print("Optimization took %.2f hours." % ((time() - start)/3600))

    # print & display main results
    model_extended.analyse_motif(X_train,y_train, THRESHOLD, nuc_type=NUC_TYPE)

    # store results and display stages
    STAGES.append('train, extended', model_extended, new_entries={'r (test)': mf.linregress(model_extended.predict(X_test),y_test).rvalue})
    mf.display_df(STAGES.df, nuc_type=NUC_TYPE)
else:
    print('I: Motif is not extended based on previous analysis.')

I: Motif is not extended based on previous analysis.

### fit & predict optimization starting with extended energy matrix plus one bordering position on each side if current bordering position exceed the information of 0.25

last_model=STAGES.df.at[max(STAGES.df.index),'model']
I_5=mf.energies2information(model_extended.energies_[0:4])>=0.25 #sufficient information content of 5' end position
I_3=mf.energies2information(model_extended.energies_[-4:])>=0.25 #sufficient information content of 3' end position

if I_5 or I_3:
    print('I: At least one of the bordering positions of the current motif has an information content of at least 0.25. Extending.')
    expanded_energies_with_border=mf.modify_energies(last_model.energies_, end5=I_5, end3=I_3)  
    mf.energies2logo(expanded_energies_with_border, nuc_type=NUC_TYPE)
    motif_length_with_border=len(expanded_energies_with_border)//4

    model_with_border=mf.findmotif(motif_length=motif_length_with_border, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS,
                   start=expanded_energies_with_border)


    start = time()
    model_with_border.fit(X_train,y_train)
    print("Optimization took %.2f hours." % ((time() - start)/3600))

    # print & display main results
    model_with_border.analyse_motif(X_train,y_train, THRESHOLD, nuc_type=NUC_TYPE)

    # store results and display stages
    STAGES.append('train, expanded, border', model_with_border, new_entries={'r (test)': mf.linregress(model_with_border.predict(X_test),y_test).rvalue})
    mf.display_df(STAGES.df, nuc_type=NUC_TYPE)
else:
    print('I: Both bordering positions of the found motif have an information content below 0.25. No futher optimization required.')

I: At least one of the bordering positions of the current motif has an information content of at least 0.25. Extending.

Optimization took 13.82 hours.
I: energy matrix and logos:

       A      C     G     T
0   459    692   142 -1294
1  5204 -12203  5272  1726
2 -8878  -1333  5133  5078
3  4942  -6978  4403 -2368
4 -1059   3959 -6608  3708
5   857    510  2563 -3931
6  2011   3067 -4934  -144
7   419   -532  -433   546

I: summed absolute energies of each position:
0     2589
1    24407
2    20423
3    18693
4    15335
5     7863
6    10156
7     1932
dtype: int64

I: averaged summed energy over all positions: 12675
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: 1634 +/- 12419
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.00004 .. 1.29615 (ratio: 32369.3)
I: number of probes: 32264
I: Pearson Correlation  r: 0.8314
I: mean absolute error: 1542.7390

I: Classification performance AUROC: 0.9889

	pos	energies	r	r under baseline	-2%
0	0	[0.0, 0.0, 0.0, -0.0, 5204.1920833228105, -122...	0.822340	-0.009062	False
1	1	[459.87578237203417, 692.1935600187725, 142.71...	0.557521	-0.273880	True
2	2	[459.87578237203417, 692.1935600187725, 142.71...	0.607805	-0.223596	True
3	3	[459.87578237203417, 692.1935600187725, 142.71...	0.507975	-0.323426	True
4	4	[459.87578237203417, 692.1935600187725, 142.71...	0.505053	-0.326349	True
5	5	[459.87578237203417, 692.1935600187725, 142.71...	0.687031	-0.144370	True
6	6	[459.87578237203417, 692.1935600187725, 142.71...	0.613651	-0.217751	True
7	7	[459.87578237203417, 692.1935600187725, 142.71...	0.828276	-0.003126	False

df_relevant_positions=last_model.explore_positions(X_train, y_train)
list_positions=df_relevant_positions.index[df_relevant_positions['-2%']].tolist() # list of positions with an increase of2% and default position 0
start_relevant=min(list_positions)
end_relevant=max(list_positions)
red5=-start_relevant
red3=end_relevant-len(df_relevant_positions)+1
print('I: The analysis suggests, that positions between %i to %i contribute significantly to the motif' %(start_relevant, end_relevant))
last_model=STAGES.df.at[max(STAGES.df.index),'model']

if (end_relevant-start_relevant+1)in STAGES.df['motif length'].to_list():
    print('I: No need for a further optimization. An optimization with motif length of %i has already been done.' %(end_relevant-start_relevant))
    print('I: Checking whether G0 has been chosen correctly.')
    last_model.investigate_G0(X_train, y_train)
else:
    print('I: Bordering positions only marginally contributing towards regression quality are dropped.')
    print('I: New start energy for motif optimization:')
    start_final_model=mf.modify_energies(last_model.energies_, end5=red5, end3=red3)
    mf.energies2logo(start_final_model, nuc_type=NUC_TYPE)
    final_model=mf.findmotif(motif_length=len(start_final_model)//4, protein_conc=PROT_CONC, 
                             both_strands=BOTH_STRANDS, start=start_final_model)

    start = time()
    final_model.fit(X_train,y_train)
    print("Optimization took %.2f hours." % ((time() - start)/3600))

    # print & display main results
    final_model.analyse_motif(X_train,y_train, THRESHOLD, nuc_type=NUC_TYPE)
    
    print('I: Checking whether G0 has been chosen correctly.')
    final_model.investigate_G0(X_train, y_train)

    # store results and display stages
    STAGES.append('train, shrinked', final_model, new_entries={'r (test)': mf.linregress(final_model.predict(X_test),y_test).rvalue})
    mf.display_df(STAGES.df, nuc_type=NUC_TYPE)  

I: The analysis suggests, that only positions between 0 to 5 contribute significantly to the motif
I: Bordering positions only marginally contributing towards regression quality are dropped.
I: New start energy for motif optimization:

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-75-d6e21e4e6261> in <module>
     21 
     22     start = time()
---> 23     final_model.fit(X_train,y_train)
     24     print("Optimization took %.2f hours." % ((time() - start)/3600))
     25 

~/fhnw/python/T7 primase site/RNAcompete/motif.py in fit(self, X, y)
    560                 res = optimize.dual_annealing(target, bounds)
    561             else:
--> 562                 res = minimize(target, start, method='Powell',tol=None, callback=None, options=options, bounds=bounds)
    563 
    564         # finalize with results of optimization

~/.local/lib/python3.8/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
    688                                    **options)
    689     elif meth == 'powell':
--> 690         res = _minimize_powell(fun, x0, args, callback, bounds, **options)
    691     elif meth == 'cg':
    692         res = _minimize_cg(fun, x0, args, jac, callback, **options)

~/.local/lib/python3.8/site-packages/scipy/optimize/_optimize.py in _minimize_powell(func, x0, args, callback, bounds, xtol, ftol, maxiter, maxfev, disp, direc, return_all, **unknown_options)
   3180                 direc1 = direc[i]
   3181                 fx2 = fval
-> 3182                 fval, x, direc1 = _linesearch_powell(func, x, direc1,
   3183                                                      tol=xtol * 100,
   3184                                                      lower_bound=lower_bound,

~/.local/lib/python3.8/site-packages/scipy/optimize/_optimize.py in _linesearch_powell(func, p, xi, tol, lower_bound, upper_bound, fval)
   2918         elif not np.isneginf(bound[0]) and not np.isposinf(bound[1]):
   2919             # we can use a bounded scalar minimization
-> 2920             res = _minimize_scalar_bounded(myfunc, bound, xatol=tol / 100)
   2921             xi = res.x * xi
   2922             return squeeze(res.fun), p + xi, xi

~/.local/lib/python3.8/site-packages/scipy/optimize/_optimize.py in _minimize_scalar_bounded(func, bounds, args, xatol, maxiter, disp, **unknown_options)
   2162         si = np.sign(rat) + (rat == 0)
   2163         x = xf + si * np.maximum(np.abs(rat), tol1)
-> 2164         fu = func(x, *args)
   2165         num += 1
   2166         fmin_data = (num, x, fu)

~/.local/lib/python3.8/site-packages/scipy/optimize/_optimize.py in myfunc(alpha)
   2901     """
   2902     def myfunc(alpha):
-> 2903         return func(p + alpha*xi)
   2904 
   2905     # if xi is zero, then don't optimize

~/.local/lib/python3.8/site-packages/scipy/optimize/_optimize.py in function_wrapper(x, *wrapper_args)
    494         ncalls[0] += 1
    495         # A copy of x is sent to the user function (gh13740)
--> 496         fx = function(np.copy(x), *(wrapper_args + args))
    497         # Ideally, we'd like to a have a true scalar returned from f(x). For
    498         # backwards-compatibility, also allow np.array([1.3]),

~/fhnw/python/T7 primase site/RNAcompete/motif.py in target(energies_ACG)
    526         def target(energies_ACG):
    527             energies=acg2acgt(energies_ACG)
--> 528             binding=self.calculate_binding(sub,G0,energies)
    529             r=linregress(y, binding).rvalue  #regression (shall be positive)
    530             penalty_base=np.sum(np.maximum(abs(energies)- self.threshold_base, 0)**2)*self.motif_length*self.penalty_base   #penalty if energy of a base is too high

~/fhnw/python/T7 primase site/RNAcompete/motif.py in calculate_binding(self, sub, G0, energies)
    446         #this is the calculation for the forward probe sequence
    447         G=sub*energies #calculate energies for each base of all subsequences
--> 448         Gsub=np.apply_along_axis(sum, 2, G)+G0 #sum-up over all positions of a subsequence for each subsequence and add G0
    449         f=lambda G: self.protein_conc/(self.protein_conc+(1/np.exp(-G/8.31/298)*1E6)) * np.exp(-self.time_dissociation*1/np.exp(-G/8.31/298)*self.kon)
    450         #  (1/np.exp(-dG/R/T)*1E6) equals to Kd [uM]

~/.local/lib/python3.8/site-packages/numpy/core/overrides.py in apply_along_axis(*args, **kwargs)

~/.local/lib/python3.8/site-packages/numpy/lib/shape_base.py in apply_along_axis(func1d, axis, arr, *args, **kwargs)
    400     buff[ind0] = res
    401     for ind in inds:
--> 402         buff[ind] = asanyarray(func1d(inarr_view[ind], *args, **kwargs))
    403 
    404     if not isinstance(res, matrix):

KeyboardInterrupt: 

mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

	stage	protein	# probes	motif length	r	AUROC	G0	G0 fitted	ratio	max binding	min binding	energies	model	logo	r (test)
0	quick	Mlx	1000	3	0.665118	0.828279	-11473.366709	False	30.220111	2.540790	0.084076	-1722,..	suppressed		NaN
1	best grid	Mlx	1000	6	0.804184	0.903057	-1946.454775	False	2264.716271	3.199344	0.001413	2367,..	suppressed		NaN
2	best repetition	Mlx	1000	6	0.890235	0.931599	-1946.454775	False	121.794730	0.172153	0.001413	-3592,..	suppressed		0.732571
3	train dataset	Mlx	32264	6	0.807686	0.981296	-1946.454775	False	2338.717644	0.569843	0.000244	-836,..	suppressed		0.805514
4	train, expanded, border	Mlx	32264	8	0.831401	0.988867	3085.476013	False	32369.323534	1.296148	0.000040	459,..	suppressed		0.817456

STAGES.df.to_json('%s_%s-%s-%s_%s-%s.json' %(PROTEIN_NAME, datetime.now().year, datetime.now().month,datetime.now().day , datetime.now().hour, datetime.now().minute))
STAGES.df.to_pickle('%s_%s-%s-%s_%s-%s.pkl' %(PROTEIN_NAME, datetime.now().year, datetime.now().month,datetime.now().day , datetime.now().hour, datetime.now().minute))

df_stages.drop(index='best grid fitG0=True', inplace=True)

"""
expanded_energies=mf.modify_energies(model_train.energies_, end5=ext5, end3=ext3)  # <==== adjust end5 and end3 to enlarge core motif on 5' and 3' end
mf.energies2logo(expanded_energies, nuc_type=NUC_TYPE)
expanded_motif_length=len(expanded_energies)//4
"""

import importlib

importlib.reload(mf)

Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)

<module 'motif' from '/home/GLipps/fhnw/python/T7 primase site/RNAcompete/motif.py'>

start = time()
model_mae=model_with_border.refit_mae(X,y)
print("Optimization took %.2f hours." % ((time() - start)/3600))

# print & display main results
model_mae.analyse_motif(X_train,y_train, THRESHOLD, nuc_type=NUC_TYPE)

# store results and display stages
STAGES.append('train, expanded, border, mae', model_mae, new_entries={'r (test)': mf.linregress(model_mae.predict(X_test),y_test).rvalue})
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)